In this Section, we first evaluate different variants of the proposed TEDGE caching framework to obtain the best architecture through the process of trial and error. Considering the location of UE, which is obtained from the ZIP code and followed by Reference [1]}, six hgNBs are employed in different areas, where the classification accuracy is averaged over all hgNBs. In all experiments, the one-dimensional time-series content's request data is converted to a sequential set of images, which is known as the Gramian Angular Field (GAF) technique [2]}. Utilizing GAF method, not only the temporal characteristics of the data is preserved, but also the temporal correlations of data are included. In Back Propagation (BP) training, Adam optimizer is employed, where the weight decay and betas are set to \(0.001\)  and (\(0.9, 0.999\) ). The size of the input image, the size of input patches, and the batch size are (\(25 \times 25\) ), (\(5\times 5\) ), and 256, respectively. Finally, we use binary cross-entropy as the loss function for our multi-label classification problem. According to the results in Table REF , increasing the model dimension from 32 to 128 (Model 1 to Model 3), and also the number of MLP layers from 1 to 3 (Models 3, 5, and 6) increase the classification accuracy, while increasing the number of trainable parameters. Moreover, we evaluate the effect of MLP size on the classification accuracy. As it can be seen from Table REF , increasing the MLP size from 256 to 512 (Model 3 and Model 9) decreases the classification accuracy. Similarly, there is no improvement in the classification accuracy by increasing the number of transformer layers (see Models 3 and 4). Furthermore, considering Model 6 to Model 8, the best number of heads in this architecture is equal to 8. Note that we also evaluated the effect of window length on the classification accuracy, while no improvement has been achieved by changing the window length.
